IA neuro-symbolique pour l'interprétation granulaire des données des bases STUPS© et OTARIES© : défis applicatifs et perspectives

6533b7cffe1ef96bd1258b8c

RESEARCH PRODUCT

IA neuro-symbolique pour l'interprétation granulaire des données des bases STUPS© et OTARIES© : défis applicatifs et perspectives

Sébastien Guillemin Laurence Dujourdy Ludovic Journeaux Ana Roxin

subject

Symbolic AI [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Artificial intelligence decision support Connectionist AI Neurosymbolic AI Artificial intelligence Symbolic AI Connectionist AI Neurosymbolic AI decision support [INFO] Computer Science [cs]

description

Historiquement, l'intelligence artificielle (IA) s'est divisée en 2 courants selon les hypothèses faites pour modéliser l'intelligence humaine : l'IA symbolique, supposant que des symboles sont nécessaires, et l'IA statistique (plus particulièrement l'IA connexionniste) affirmant le contraire. Dernièrement, l'IA neuro-symbolique tente de réconcilier les 2. Les travaux présentés dans cet article sont en en collaboration avec la Police Scientifique française, dans le contexte du Plan National Stup. Nous présentons les problématiques métiers en lien avec le projet, puis en déduisons les problématiques scientifiques. Après un rappel des domaines de l'IA et leurs limites, nous présentons un état des lieux du domaine de l'IA neuro-symbolique. Nous discutons les défis applicatifs avant de finir par une présentation des premiers travaux qui ont été conduits. Des pistes d'approches basées sur l'état des lieux de l'IA neurosymbolique sont aussi présentées pour la suite des travaux.

year	journal	country	edition	language
2023-01-01

https://hal.science/hal-04157724

PEPhysical Sciences and Engineering
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  - PE2_16Non-linear physics
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  - LS3_17Theoretical modelling in cellular, developmental and regenerative biology
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  - LS5_8Neural basis of behaviour (e.g. sleep, consciousness, addiction)
  - LS5_4Neural stem cells
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